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Journal of Cryptology

, Volume 28, Issue 2, pp 351–395 | Cite as

Computing on Authenticated Data

  • Jae Hyun Ahn
  • Dan Boneh
  • Jan Camenisch
  • Susan Hohenberger
  • Abhi Shelat
  • Brent Waters
Article

Abstract

In tandem with recent progress on computing on encrypted data via fully homomorphic encryption, we present a framework for computing on authenticated data via the notion of slightly homomorphic signatures, or \(P\)-homomorphic signatures. With such signatures, it is possible for a third party to derive a signature on the object \(m'\) from a signature of \(m\) as long as \(P(m,m')=1\) for some predicate \(P\) which captures the “authenticatable relationship" between \(m'\) and \(m\). Moreover, a derived signature on \(m'\) reveals no extra information about the parent \(m\). Our definition is carefully formulated to provide one unified framework for a variety of distinct concepts in this area, including arithmetic, homomorphic, quotable, redactable, transitive signatures, and more. It includes being unable to distinguish a derived signature from a fresh one even when given the original signature. The inability to link derived signatures to their original sources prevents some practical privacy and linking attacks, which is a challenge not satisfied by most prior works. Under this strong definition, we then provide generic constructions for all univariate and closed predicates, and specific efficient constructions for a broad class of natural predicates such as quoting, subsets, weighted sums, averages, and Fourier transforms. To our knowledge, these are the first efficient constructions for these predicates (excluding subsets) that provably satisfy this strong security notion.

Keywords

Authentication Homomorphic signatures Quotable signatures 

Notes

Acknowledgments

We are grateful to the anonymous reviewers of TCC 2012 and the Journal of Cryptology for their helpful comments.

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Copyright information

© International Association for Cryptologic Research 2014

Authors and Affiliations

  • Jae Hyun Ahn
    • 1
  • Dan Boneh
    • 2
  • Jan Camenisch
    • 3
  • Susan Hohenberger
    • 1
  • Abhi Shelat
    • 4
  • Brent Waters
    • 5
  1. 1.Johns Hopkins UniversityBaltimoreUSA
  2. 2.Stanford UniversityStanfordUSA
  3. 3.IBM Research – ZurichRuschlikonSwitzerland
  4. 4.University of VirginiaCharlottesvilleUSA
  5. 5.University of Texas at AustinAustinUSA

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